Influence of ULS acquisition characteristics on tree stem parameter estimation

Bruggisser, Moritz; Hollaus, Markus; Otepka, Johannes; Pfeifer, Norbert

doi:10.1016/j.isprsjprs.2020.08.002

Cited by 23 publications

(21 citation statements)

References 45 publications

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“…The sensitivity analysis with increasing raster cell sizes as a proxy for decreasing point density suggests that our best models could perform similarly using ALS data instead of TLS, providing point density is greater than 16 pts/m 2 . While the point density of ALS and DLS (laser scanning from drones) point clouds is dependent on the flight altitude and speed, number of flight lines, and scan and pulse rates, the 16 pts/m 2 threshold is attainable by both ALS and DLS [85][86][87]. This threshold is also consistent with the one suggested in a recent study for height estimation of coniferous trees using drone-based LiDAR point clouds of different point densities, which found that height accuracy only worsens at below 17 pts/m 2 [73].…”

Section: Crown Area Sensitivity Analysismentioning

confidence: 99%

Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees

Wagers

Castilla

Filiatrault

et al. 2021

Forests

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Research Highlights: This study advances the effort to accurately estimate the biomass of trees in peatlands, which cover 13% of Canada’s land surface. Background and Objectives: Trees remove carbon from the atmosphere and store it as biomass. Terrestrial laser scanning (TLS) has become a useful tool for modelling forest structure and estimating the above ground biomass (AGB) of trees. Allometric equations are often used to estimate individual tree AGB as a function of height and diameter at breast height (DBH), but these variables can often be laborious to measure using traditional methods. The main objective of this study was to develop allometric equations using TLS-measured variables and compare their accuracy with that of other widely used equations that rely on DBH. Materials and Methods: The study focusses on small black spruce trees (<5 m) located in peatland ecosystems of the Taiga Plains Ecozone in the Northwest Territories, Canada. Black spruce growing in peatlands are often stunted when compared to upland black spruce and having models specific to them would allow for more precise biomass estimates. One hundred small trees were destructively sampled from 10 plots and the dry weight of each tree was measured in the lab. With this reference data, we fitted biomass models specific to peatland black spruce using DBH, crown diameter, crown area, height, tree volume, and bounding box volume as predictors. Results: Our best models had crown size and height as predictors and outperformed established AGB equations that rely on DBH. Conclusions: Our equations are based on predictors that can be measured from above, and therefore they may enable the plotless creation of accurate biomass reference data for a prominent tree species in a common ecosystem (treed peatlands) in North America’s boreal.

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Section: Crown Area Sensitivity Analysismentioning

confidence: 99%

Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees

Wagers

Castilla

Filiatrault

et al. 2021

Forests

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“…Above canopy ULS and under canopy ULS were implemented and studied separately in existing applications. According to (Bruggisser et al 2020), half of the stem circumference is required in above canopy ULS data in order to achieve reliable DBH estimates. However, to capture half of the stem circumference for all standing trees in forest using above canopy ULS is a demanding task (Liang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

et al. 2021

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Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.

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“…Method #3 can only be applied under the most restrictive point cloud properties requiring a high-density point cloud; the base of tree stems must be clearly identifiable to apply the cylinder-fitting algorithm [72]. Therefore, this method can mostly be applied when acquiring high-density ULS data under leaf-off conditions.…”

Section: Forest Inventory Attributes Of An Uneven-aged Hardwood Stand Using Uls Datamentioning

confidence: 99%

“…Such areas may be costly for an ALS data collection [63] or require too much time for full coverage by ground-based units [64]. More and more studies have demonstrated that ULS provides accurate data with good repeatability [58,[65][66][67][68] and has great potential to support forest inventories (e.g., [57,[66][67][68][69][70][71][72]). Transferability of ITD to ULS data is now actively being investigated (e.g., [73][74][75][76][77]).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

et al. 2021

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UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.

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Influence of ULS acquisition characteristics on tree stem parameter estimation

Cited by 23 publications

References 45 publications

Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees

Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees

Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

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